An adaptive control system (ACS) uses direct output feedback to control a plant.
The ACS uses direct adaptive output feedback control developed for highly uncertain
nonlinear systems, that does not rely on state estimation. The approach is also
applicable to systems of unknown, but bounded dimension, whose output has known,
but otherwise arbitrary relative degree. This includes systems with both parameter
uncertainty and unmodeled dynamics. The result is achieved by extending the universal
function approximation property of linearly parameterized neural networks to model
unknown system dynamics from input/output data. The network weight adaptation rule
is derived from Lyapunov stability analysis, and guarantees that the adapted weight
errors and the tracking error are bounded.
